Adaptive Deep Learning Ensemble Framework for Real-Time Network Anomaly Detection

Abstract

In rapid development of computer networks, anomaly detection is essential to securing modern communication networks against emerging cyber-attacks, zero-day vulnerabilities, and massive malicious traffic. But conventional intrusion detection systems are plagued by high false alarms, scalability issues and low adaptability to evolving network conditions. To overcome these challenges, this research suggests an adaptive deep learning and ensemble model for scalable real-time detection of network anomalies. This approach combines CNNs for spatial feature learning, RNNs for temporal pattern recognition, and dynamically weighted ensemble learning for making reliable decisions. The model uses normalization, feature selection and class-balancing to enhance generalization. The model is tested across benchmark datasets (CICIDS2017, NSL-KDD, UNSW-NB15) under various attack scenarios. The proposed framework achieves 97.83% accuracy, 97.8% precision, 97.6% recall, 97.7% F1-score and surpasses traditional machine learning and pure deep learning approaches. Further, the model exhibits low inference times and better adaptability to concept drift. This system can be deployed in enterprise, IoT, and edge-security systems for efficient, scalable, and intelligent threat detection.